{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "creative-journalist",
   "metadata": {},
   "source": [
    "## 1 介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wicked-millennium",
   "metadata": {},
   "source": [
    "## 2 模型验证"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accessory-mason",
   "metadata": {},
   "source": [
    "### 2.0 准备阶段\n",
    "\n",
    "#### 2.0.1 导入必要的包和库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "naughty-cooling",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# 打印不省略\n",
    "pd.set_option('display.max_columns',None) # 列\n",
    "pd.set_option('display.max_rows',None) # 行\n",
    "import numpy as np\n",
    "import pickle\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import time\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 这是验证代码\n",
    "# train_path='../../datasets/cut/round2/round2_train_cut_by_type.txt'\n",
    "# # train_path_t='../../datasets/cut/round1/round1_train_cut_by_type.txt'\n",
    "# # test_path_a='../../datasets/cut/round1/round1_ijcai_18_test_a_20180301.txt'\n",
    "# # test_path_b='../../datasets/cut/round1/round1_ijcai_18_test_b_20180418.txt'\n",
    "# datapath = '../../produce/mergeData.csv'\n",
    "\n",
    "# data = pd.read_csv(train_path ,sep=' ')\n",
    "\n",
    "# # 将timestamp转换成datetime【%Y-%m-%d %H:%M:%S】\n",
    "# def timestamp_datetime(value):\n",
    "#     format = '%Y-%m-%d %H:%M:%S'\n",
    "#     value = time.localtime(value)\n",
    "#     dt = time.strftime(format, value)\n",
    "#     return dt # str\n",
    "    \n",
    "# # 时间，datetime64[ns]\n",
    "# data['time'] = pd.to_datetime(data.context_timestamp.apply(timestamp_datetime))\n",
    "# data['month'] = data.time.dt.month\n",
    "# data['day'] = data.time.dt.day\n",
    "# # data['hour'] = data.time.dt.hour\n",
    "\n",
    "# data.groupby(['month', 'day']).count().to_csv('../../produce/count.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advance-eclipse",
   "metadata": {},
   "source": [
    "### 2.1 验证过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "strategic-filing",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "datapath = '../../produce/featureData.csv'\n",
    "data = pd.read_csv(datapath ,sep=' ')\n",
    "\n",
    "# 解决模型不能处理非英文字符特征的问题\n",
    "import re\n",
    "data = data.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))\n",
    "\n",
    "filename = '../../produce/serialize_constant'\n",
    "\n",
    "with open(filename, 'rb') as f:  \n",
    "    serialize_constant = pickle.load(f)\n",
    "    trainLen = serialize_constant['trainLen']\n",
    "    trainlabel = serialize_constant['trainlabel']\n",
    "    testInstanceID = serialize_constant['testInstanceID']\n",
    "\n",
    "data = data.iloc[0 : trainLen, : ]\n",
    "target = trainlabel\n",
    "\n",
    "# model.modelFiveFoldEval(data, target) # 865 行，ValueError: Input contains NaN, infinity or a value too large for dtype('float32').\n",
    "\n",
    "data['target'] = np.array(target)\n",
    "\n",
    "loglossList = []\n",
    "avglogloss = 0\n",
    "foldNum = 1\n",
    "\n",
    "# print(data['day'].sort_values())\n",
    "\n",
    "day24 = data[data['day'] == 24]\n",
    "\n",
    "day18_23 = data[data['day'] < 24]\n",
    "day19_23 = day18_23[data['day'] >= 18]\n",
    "\n",
    "dataTrain = day19_23.drop(['target'], axis=1)\n",
    "labelTrain = day19_23['target']\n",
    "\n",
    "dataTest = day24.drop(['target'], axis=1)\n",
    "labelTest = day24['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2 验证方式一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "here\n[0]\ttrain-logloss:0.644386\ttest-logloss:0.644417\nMultiple eval metrics have been passed: 'test-logloss' will be used for early stopping.\n\nWill train until test-logloss hasn't improved in 150 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# print(dataTrain)\n",
    "# print(labelTrain)\n",
    "# print(dataTest)\n",
    "# print(labelTest)\n",
    "print('here')\n",
    "\n",
    "dtrain = xgb.DMatrix(data=dataTrain.values, label=labelTrain.values)\n",
    "dtest = xgb.DMatrix(data=dataTest.values, label=labelTest.values)\n",
    "\n",
    "watchlist = [(dtrain, 'train'), (dtest, 'test')]\n",
    "progress = dict()\n",
    "# xgbparamSetting()\n",
    "param = {\n",
    "            'learning_rate': 0.05,\n",
    "            'eta': 0.4,\n",
    "            'max_depth': 3,\n",
    "            'gamma': 0,\n",
    "            'subsample': 0.8,\n",
    "            'colsample_bytree': 0.8,\n",
    "            'alpha': 1,\n",
    "            # 'lambda' : 0.1,\n",
    "            'nthread': 4,\n",
    "            'objective': 'binary:logistic',\n",
    "            'eval_metric': 'logloss'\n",
    "        }\n",
    "# treeNum\n",
    "num_boost_round = 3000\n",
    "\n",
    "bst = xgb.train(param, dtrain, num_boost_round, watchlist, early_stopping_rounds=150, evals_result=progress)\n",
    "\n",
    "bst.save_model('../../produce/xgbModel' + str(foldNum))\n",
    "\n",
    "xgb.plot_importance(bst)\n",
    "\n",
    "logList = progress['test']['logloss']\n",
    "\n",
    "tmplogloss = np.min(np.array(logList))\n",
    "avglogloss = avglogloss + tmplogloss\n",
    "loglossList.append(logList)\n",
    "\n",
    "num = len(loglossList)\n",
    "val = len(loglossList[0])\n",
    "result = []\n",
    "for j in range(val):\n",
    "    cal = 0\n",
    "    for i in range(num):\n",
    "        tmplist = loglossList[i]\n",
    "        cal = cal + tmplist[j]\n",
    "        cal = cal / num\n",
    "        result.append(cal)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.3 验证方式二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[LightGBM] [Info] Number of positive: 612, number of negative: 28880\n[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050670 seconds.\nYou can set `force_col_wise=true` to remove the overhead.\n[LightGBM] [Info] Total Bins 8625\n[LightGBM] [Info] Number of data points in the train set: 29492, number of used features: 95\n[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.020751 -> initscore=-3.854172\n[LightGBM] [Info] Start training from score -3.854172\n[1]\tvalid_0's binary_logloss: 0.0768261\nTraining until validation scores don't improve for 500 rounds\n[2]\tvalid_0's binary_logloss: 0.0768301\n[3]\tvalid_0's binary_logloss: 0.0766405\n[4]\tvalid_0's binary_logloss: 0.0700899\n[5]\tvalid_0's binary_logloss: 0.0657661\n[6]\tvalid_0's binary_logloss: 0.0657095\n[7]\tvalid_0's binary_logloss: 0.0657063\n[8]\tvalid_0's binary_logloss: 0.0655657\n[9]\tvalid_0's binary_logloss: 0.0653346\n[10]\tvalid_0's binary_logloss: 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7.58097e-05\n[2791]\tvalid_0's binary_logloss: 7.59718e-05\n[2792]\tvalid_0's binary_logloss: 7.52211e-05\n[2793]\tvalid_0's binary_logloss: 7.5174e-05\n[2794]\tvalid_0's binary_logloss: 7.44311e-05\n[2795]\tvalid_0's binary_logloss: 7.36955e-05\n[2796]\tvalid_0's binary_logloss: 7.29671e-05\n[2797]\tvalid_0's binary_logloss: 7.22458e-05\n[2798]\tvalid_0's binary_logloss: 7.15316e-05\n[2799]\tvalid_0's binary_logloss: 7.17234e-05\n[2800]\tvalid_0's binary_logloss: 7.18028e-05\n[2801]\tvalid_0's binary_logloss: 7.19839e-05\n[2802]\tvalid_0's binary_logloss: 7.21914e-05\n[2803]\tvalid_0's binary_logloss: 7.21671e-05\n[2804]\tvalid_0's binary_logloss: 7.22722e-05\n[2805]\tvalid_0's binary_logloss: 7.24634e-05\n[2806]\tvalid_0's binary_logloss: 7.17472e-05\n[2807]\tvalid_0's binary_logloss: 7.10379e-05\n[2808]\tvalid_0's binary_logloss: 7.03357e-05\n[2809]\tvalid_0's binary_logloss: 7.0334e-05\n[2810]\tvalid_0's binary_logloss: 7.03785e-05\n[2811]\tvalid_0's binary_logloss: 7.03779e-05\n[2812]\tvalid_0's binary_logloss: 7.04016e-05\n[2813]\tvalid_0's binary_logloss: 6.97055e-05\n[2814]\tvalid_0's binary_logloss: 6.90163e-05\n[2815]\tvalid_0's binary_logloss: 6.83339e-05\n[2816]\tvalid_0's binary_logloss: 6.76582e-05\n[2817]\tvalid_0's binary_logloss: 6.78594e-05\n[2818]\tvalid_0's binary_logloss: 6.71883e-05\n[2819]\tvalid_0's binary_logloss: 6.72187e-05\n[2820]\tvalid_0's binary_logloss: 6.72583e-05\n[2821]\tvalid_0's binary_logloss: 6.73591e-05\n[2822]\tvalid_0's binary_logloss: 6.74203e-05\n[2823]\tvalid_0's binary_logloss: 6.75256e-05\n[2824]\tvalid_0's binary_logloss: 6.76186e-05\n[2825]\tvalid_0's binary_logloss: 6.75885e-05\n[2826]\tvalid_0's binary_logloss: 6.69201e-05\n[2827]\tvalid_0's binary_logloss: 6.62582e-05\n[2828]\tvalid_0's binary_logloss: 6.63876e-05\n[2829]\tvalid_0's binary_logloss: 6.64563e-05\n[2830]\tvalid_0's binary_logloss: 6.63509e-05\n[2831]\tvalid_0's binary_logloss: 6.56946e-05\n[2832]\tvalid_0's binary_logloss: 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6.23618e-05\n[2854]\tvalid_0's binary_logloss: 6.17448e-05\n[2855]\tvalid_0's binary_logloss: 6.176e-05\n[2856]\tvalid_0's binary_logloss: 6.11489e-05\n[2857]\tvalid_0's binary_logloss: 6.05439e-05\n[2858]\tvalid_0's binary_logloss: 5.99448e-05\n[2859]\tvalid_0's binary_logloss: 5.98997e-05\n[2860]\tvalid_0's binary_logloss: 5.99471e-05\n[2861]\tvalid_0's binary_logloss: 5.98961e-05\n[2862]\tvalid_0's binary_logloss: 5.99316e-05\n[2863]\tvalid_0's binary_logloss: 5.93385e-05\n[2864]\tvalid_0's binary_logloss: 5.87512e-05\n[2865]\tvalid_0's binary_logloss: 5.88392e-05\n[2866]\tvalid_0's binary_logloss: 5.88959e-05\n[2867]\tvalid_0's binary_logloss: 5.8313e-05\n[2868]\tvalid_0's binary_logloss: 5.83437e-05\n[2869]\tvalid_0's binary_logloss: 5.84277e-05\n[2870]\tvalid_0's binary_logloss: 5.83856e-05\n[2871]\tvalid_0's binary_logloss: 5.78077e-05\n[2872]\tvalid_0's binary_logloss: 5.78528e-05\n[2873]\tvalid_0's binary_logloss: 5.78475e-05\n[2874]\tvalid_0's binary_logloss: 5.80296e-05\n[2875]\tvalid_0's binary_logloss: 5.81252e-05\n[2876]\tvalid_0's binary_logloss: 5.81592e-05\n[2877]\tvalid_0's binary_logloss: 5.81793e-05\n[2878]\tvalid_0's binary_logloss: 5.76035e-05\n[2879]\tvalid_0's binary_logloss: 5.77245e-05\n[2880]\tvalid_0's binary_logloss: 5.76941e-05\n[2881]\tvalid_0's binary_logloss: 5.76571e-05\n[2882]\tvalid_0's binary_logloss: 5.70864e-05\n[2883]\tvalid_0's binary_logloss: 5.70782e-05\n[2884]\tvalid_0's binary_logloss: 5.69925e-05\n[2885]\tvalid_0's binary_logloss: 5.70451e-05\n[2886]\tvalid_0's binary_logloss: 5.70173e-05\n[2887]\tvalid_0's binary_logloss: 5.71775e-05\n[2888]\tvalid_0's binary_logloss: 5.66115e-05\n[2889]\tvalid_0's binary_logloss: 5.6626e-05\n[2890]\tvalid_0's binary_logloss: 5.66926e-05\n[2891]\tvalid_0's binary_logloss: 5.67455e-05\n[2892]\tvalid_0's binary_logloss: 5.67188e-05\n[2893]\tvalid_0's binary_logloss: 5.67429e-05\n[2894]\tvalid_0's binary_logloss: 5.67512e-05\n[2895]\tvalid_0's binary_logloss: 5.68229e-05\n[2896]\tvalid_0's binary_logloss: 5.6778e-05\n[2897]\tvalid_0's binary_logloss: 5.67828e-05\n[2898]\tvalid_0's binary_logloss: 5.69205e-05\n[2899]\tvalid_0's binary_logloss: 5.6357e-05\n[2900]\tvalid_0's binary_logloss: 5.57991e-05\n[2901]\tvalid_0's binary_logloss: 5.52467e-05\n[2902]\tvalid_0's binary_logloss: 5.46998e-05\n[2903]\tvalid_0's binary_logloss: 5.41582e-05\n[2904]\tvalid_0's binary_logloss: 5.41788e-05\n[2905]\tvalid_0's binary_logloss: 5.419e-05\n[2906]\tvalid_0's binary_logloss: 5.41335e-05\n[2907]\tvalid_0's binary_logloss: 5.41751e-05\n[2908]\tvalid_0's binary_logloss: 5.40883e-05\n[2909]\tvalid_0's binary_logloss: 5.35528e-05\n[2910]\tvalid_0's binary_logloss: 5.30225e-05\n[2911]\tvalid_0's binary_logloss: 5.31154e-05\n[2912]\tvalid_0's binary_logloss: 5.25894e-05\n[2913]\tvalid_0's binary_logloss: 5.26714e-05\n[2914]\tvalid_0's binary_logloss: 5.21498e-05\n[2915]\tvalid_0's binary_logloss: 5.21834e-05\n[2916]\tvalid_0's binary_logloss: 5.22219e-05\n[2917]\tvalid_0's binary_logloss: 5.17047e-05\n[2918]\tvalid_0's binary_logloss: 5.16816e-05\n[2919]\tvalid_0's binary_logloss: 5.17693e-05\n[2920]\tvalid_0's binary_logloss: 5.18132e-05\n[2921]\tvalid_0's binary_logloss: 5.18675e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2922]\tvalid_0's binary_logloss: 5.13539e-05\n[2923]\tvalid_0's binary_logloss: 5.08452e-05\n[2924]\tvalid_0's binary_logloss: 5.08076e-05\n[2925]\tvalid_0's binary_logloss: 5.08021e-05\n[2926]\tvalid_0's binary_logloss: 5.0717e-05\n[2927]\tvalid_0's binary_logloss: 5.07182e-05\n[2928]\tvalid_0's binary_logloss: 5.02159e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2929]\tvalid_0's binary_logloss: 5.0169e-05\n[2930]\tvalid_0's binary_logloss: 4.9672e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2931]\tvalid_0's binary_logloss: 4.96668e-05\n[2932]\tvalid_0's binary_logloss: 4.97085e-05\n[2933]\tvalid_0's binary_logloss: 4.97332e-05\n[2934]\tvalid_0's binary_logloss: 4.92405e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2935]\tvalid_0's binary_logloss: 4.87528e-05\n[2936]\tvalid_0's binary_logloss: 4.82698e-05\n[2937]\tvalid_0's binary_logloss: 4.8296e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2938]\tvalid_0's binary_logloss: 4.78175e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2939]\tvalid_0's binary_logloss: 4.77414e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2940]\tvalid_0's binary_logloss: 4.72684e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2941]\tvalid_0's binary_logloss: 4.72388e-05\n[2942]\tvalid_0's binary_logloss: 4.71783e-05\n[2943]\tvalid_0's binary_logloss: 4.72285e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: 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-inf\n[2968]\tvalid_0's binary_logloss: 4.29425e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2969]\tvalid_0's binary_logloss: 4.30041e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2970]\tvalid_0's binary_logloss: 4.25779e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2971]\tvalid_0's binary_logloss: 4.26639e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2972]\tvalid_0's binary_logloss: 4.27371e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2973]\tvalid_0's binary_logloss: 4.23135e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2974]\tvalid_0's binary_logloss: 4.1894e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2975]\tvalid_0's binary_logloss: 4.14788e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2976]\tvalid_0's binary_logloss: 4.15046e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2977]\tvalid_0's binary_logloss: 4.10931e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2978]\tvalid_0's binary_logloss: 4.06858e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2979]\tvalid_0's binary_logloss: 4.02824e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2980]\tvalid_0's binary_logloss: 4.02657e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2981]\tvalid_0's binary_logloss: 3.98665e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2982]\tvalid_0's binary_logloss: 3.98067e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2983]\tvalid_0's binary_logloss: 3.9412e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2984]\tvalid_0's binary_logloss: 3.90212e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2985]\tvalid_0's binary_logloss: 3.89854e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2986]\tvalid_0's binary_logloss: 3.90456e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2987]\tvalid_0's binary_logloss: 3.86585e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2988]\tvalid_0's binary_logloss: 3.86696e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2989]\tvalid_0's binary_logloss: 3.86735e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2990]\tvalid_0's binary_logloss: 3.829e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2991]\tvalid_0's binary_logloss: 3.79103e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2992]\tvalid_0's binary_logloss: 3.7954e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2993]\tvalid_0's binary_logloss: 3.79884e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2994]\tvalid_0's binary_logloss: 3.76117e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2995]\tvalid_0's binary_logloss: 3.75894e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2996]\tvalid_0's binary_logloss: 3.76313e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2997]\tvalid_0's binary_logloss: 3.72582e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2998]\tvalid_0's binary_logloss: 3.72971e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[2999]\tvalid_0's binary_logloss: 3.72447e-05\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[3000]\tvalid_0's binary_logloss: 3.72197e-05\nDid not meet early stopping. Best iteration is:\n[3000]\tvalid_0's binary_logloss: 3.72197e-05\n[0.3222085, 0.19951729737585222, 0.3001605, 0.18849529605026644, 0.2816255, 0.17913298599014388, 0.2631985, 0.1666441806683916, 0.2476965, 0.15673131503230264, 0.233414, 0.1495617519865364, 0.218914, 0.142310136666619, 0.205525, 0.13554536668696032, 0.193135, 0.12923478229266602, 0.181646, 0.12196071841461396, 0.171978, 0.11712555764504398, 0.161971, 0.11212494492398412, 0.1535245, 0.10658339153192582, 0.14476, 0.10218237200406173, 0.136572, 0.09700327821869939, 0.1289145, 0.09311938925652753, 0.121744, 0.08950154624574107, 0.1157735, 0.08554843953186822, 0.1094205, 0.08234982937785487, 0.103456, 0.07937085721720605, 0.097852, 0.07656857445547573, 0.0925835, 0.07392731110243764, 0.087625, 0.07143997180744917, 0.082957, 0.06910350727810663, 0.0790555, 0.06713510413058328, 0.07488, 0.06502759230164264, 0.070942, 0.06303489164246653, 0.067227, 0.06113138695541491, 0.0641485, 0.05957964746132147, 0.060812, 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    }
   ],
   "source": [
    "dtrain = lgb.Dataset(dataTrain, label=labelTrain)\n",
    "dtest = lgb.Dataset(dataTest, label=labelTest)\n",
    "\n",
    "process = dict()\n",
    "# LGBMparamSetting()\n",
    "param = {\n",
    "            'learning_rate': 0.01,\n",
    "            'num_leaves': 32,\n",
    "            # 'eta' : 0.4,\n",
    "            'subsample': 0.35,\n",
    "            'colsample_bytree': 0.3,\n",
    "            'nthread': 4,\n",
    "            # 'lambda_l1' : 0.1,\n",
    "            'objective': 'binary',\n",
    "            'metric': 'binary_logloss'\n",
    "        }\n",
    "# treeNum\n",
    "num_boost_round = 3000\n",
    "\n",
    "bst = lgb.train(param, dtrain, num_boost_round, valid_sets=dtest, early_stopping_rounds=500, evals_result=process)\n",
    "\n",
    "bst.save_model('../../produce/lgbModel' + str(foldNum))\n",
    "\n",
    "logList = process['valid_0']['binary_logloss']\n",
    "\n",
    "# 特征分数\n",
    "score = bst.feature_importance()\n",
    "name = bst.feature_name()\n",
    "feature_score = sorted(zip(name, score), key=lambda a: a[1], reverse=True)\n",
    "with open(\"../../produce/feature_score.txt\", \"a\") as f:\n",
    "    f.write(str(param) + '\\n')\n",
    "    f.write(str(bst.best_score) + str(bst.best_iteration) + '\\n')\n",
    "    f.write(str(feature_score) + '\\n')\n",
    "\n",
    "tmplogloss = np.min(np.array(logList))\n",
    "avglogloss = avglogloss + tmplogloss\n",
    "loglossList.append(logList)\n",
    "\n",
    "num = len(loglossList)\n",
    "val = len(loglossList[0])\n",
    "result = []\n",
    "for j in range(val):\n",
    "    cal = 0\n",
    "    for i in range(num):\n",
    "        tmplist = loglossList[i]\n",
    "        cal = cal + tmplist[j]\n",
    "        cal = cal / num\n",
    "        result.append(cal)\n",
    "print(result)"
   ]
  }
 ],
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